Abstract: With the popularity of dual cameras in recently released smart phones, a
growing number of super-resolution (SR) methods have been proposed to enhance
the resolution of stereo image pairs. However, the lack of high-quality stereo
datasets has limited the research in this area. To facilitate the training and
evaluation of novel stereo SR algorithms, in this paper, we present a
large-scale stereo dataset named Flickr1024, which contains 1024 pairs of
high-quality images and covers diverse scenarios. We first introduce the data
acquisition and processing pipeline, and then compare several popular stereo
datasets. Finally, we conduct crossdataset experiments to investigate the
potential benefits introduced by our dataset. Experimental results show that,
as compared to the KITTI and Middlebury datasets, our Flickr1024 dataset can
help to handle the over-fitting problem and significantly improves the
performance of stereo SR methods. The Flickr1024 dataset is available online
at: this https URL.